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Can We Deploy Health Information Technology that Safely Brings the Benefits of Genetics to Far More Patients? How Quickly Can We Do So?

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Presentation on theme: "Can We Deploy Health Information Technology that Safely Brings the Benefits of Genetics to Far More Patients? How Quickly Can We Do So?"— Presentation transcript:

1 Displaying and Integrating Genetic Information Through the EHR Action Collaborative DIGITizE AC

2 Can We Deploy Health Information Technology that Safely Brings the Benefits of Genetics to Far More Patients? How Quickly Can We Do So?

3 That will be helpful now but also stand the test of time?
Can We Create Inter-institutional Foundational Health Information Technology Infrastructure that Increases the Power of Genetics That will be helpful now but also stand the test of time?

4 Strategy Assemble Stakeholders Identify areas of agreement
Transform into an inter-institutional project coordination group

5 Stakeholders Government Providers Laboratories Vendors
Patients Representatives Standards Organizations

6 Membership Sandy Aronson, Partners HealthCare J.D. Nolen, Cerner
Mark Adams, Good Start Genetics Gil Alterovitz, Harvard Medical School Brian Anderson, athenahealth Jane Atkinson, NIDCR Larry Babb, Partners HealthCare Dixie Baker, Martin, Blanck and Associates Gillian Bell, Moffitt Cancer Center Chris Chute, Johns Hopkins University Chris Coffin, Invitae Mauricio De Castro, U.S. Air Force Carol Edgington, McKesson Laurel Estabrooks, Soft Computer Corporation Robert Freimuth, Mayo Clinic Geoff Ginsburg, Duke University Jennifer Hall, University of Minnesota Stephanie Hallam, Good Start Genetics Heather Halvorson, U.S. Air Force Gillian Hooker, NextGxDx Stan Huff, Intermountain Healthcare Kristen Janes, Kaiser Permanente  Andrew Kasarskis, Mount Sinai School of Medicine  Anthony Kerlavage, NCI Deborah Lange-Kuitse, McKesson  Debra Leonard, University of Vermont Steve Lincoln, Invitae  Ira Lubin, CDC  Elaine Lyon, ARUP Laboratories John Mattison, Kaiser Larry Meyer, VA  Blackford Middleton, Vanderbilt University  Doug Moeller, McKesson  Scott Moss, Epic  James O'Leary, Genetic Alliance  Erin Payne, Northrop Grumman  Brian Pech, Kaiser Permanente Teji Rakhra-Burris, Duke University   Priyadarshini Ravindran, Allscripts  Mary Relling, St. Jude Children's Research Hospital Wendy Rubinstein, NCBI Hoda Sayed-Friel, Meditech  Megan Schmidt, Sunquest Information Systems  Jud Schneider, NextGxDx Sam Shekar, Northrop Grumman  Brian Shirts, University of Washington  Brad Strock, Epic  Jeff Struewing, NHGRI  Charles Tuchinda, First Databank  Deepak Voora, Duke University Michael Watson, ACMG Scott Weiss, Partners HealthCare  Jon White, ONC Bob Wildin, NHGRI  Ken Wiley, NHGRI  Marc Williams, Geisinger  Grant Wood, Intermountain Healthcare

7 Identify Areas of Agreement
Framework for Increasing Support for Genetics in the EHR Ecosystem Germline Use Case Patterns Somatic Use Case Patterns PGx Use Case Patterns

8 Simple use cases are good for this
Objective Learn how to work together while producing near term benefit for patients Simple use cases are good for this

9 Don’t Boil the Ocean Boil some initial cups while standing on firm ground Framework for Increasing Support for Genetics in the EHR Ecosystem PGx Use Case Patterns Germline Use Case Patterns Somatic Use Case Patterns Initial PGx Use Case Types Initial PGx Use Case Types Specific Example Specific Example

10 Abacavir – HLA-B57:01 Approximately 6% of patient are hypersensitive to Abacavir Hypersensitivity can produce life threatening reaction Genetic test can predict hypersensitivity Martin et al, 2012 CPIC Guidelines

11 Thiopurine - TPMT Metabolic effect
Prescribing too high a dose places patient at risk for myelosuppression Test is required to accurately dose Reilling et al, 2011 CPIC Guideline

12 Key Pharmacogenomic Use Cases Types
# Use Case Types 1 Incorporating Genetic Results into EHR User Interfaces 2 Adding genetic tests in order sets 3 Clinical Decision Support (CDS) identifies when a test should be ordered (pre-test alert*) 4 CDS identifes when a drug order is inconsistent with a test result (post-order alert*) * Note pre and post order status refers to the status of the test order as opposed to the drug order

13 Project Coordination

14 Example of Cross Institutional Dependencies
A use case calls for providers to implement a CDS rule that requires data from the EHR ecosystem To supply the required data, EHR ecosystem vendors need to receive data from lab system vendors To instantiate the required data flows, lab and EHR ecosystem vendors need better defined standards Standards organizations need feedback from lab and provider organizations to produce needed refinements The Action Collaborative has the breadth of membership required to manage these types of issues

15 Interdependency Data Providers Labs

16 Interoperability and functionality
Interdependency Data Providers Labs Interoperability and functionality EHR Vendors LIS Vendors Supporting Vendors

17 Interoperability and functionality
Interdependency Data Providers Labs Interoperability and functionality EHR Vendors Cooperation / Interfaces LIS Vendors Supporting Vendors

18 Interdependency Data Providers Labs Interoperability and functionality
EHR Vendors Cooperation / Interfaces LIS Vendors Supporting Vendors Standards and Ontologies Standards & Ontology Organizations

19 Interdependency Data Providers Labs Interoperability and functionality
EHR Vendors Cooperation / Interfaces LIS Vendors Supporting Vendors Input Standards and Ontologies Standards & Ontology Organizations

20 Interdependency Data Providers Labs Interoperability and functionality
EHR Vendors Proof of what is possible/helpful Cooperation / Interfaces Gov Agencies LIS Vendors Supporting Vendors Input Standards and Ontologies Standards & Ontology Organizations

21 Interdependency Funding / Reimbursement Environment that Makes this Possible Data Providers Labs Interoperability and functionality EHR Vendors Proof of what is possible/helpful Cooperation / Interfaces Gov Agencies LIS Vendors Supporting Vendors Input Standards and Ontologies Standards & Ontology Organizations

22 Standards & Ontology Organizations
The Good News Providers Labs EHR Vendors Patients Cooperation / Interfaces LIS Vendors Gov Agencies Supporting Vendors Standards & Ontology Organizations

23 Lucky Choice in Baseline Rules
Not dependent on structured variant transfer Warn every time potentially desirable Existing standards work

24 Implementation Guide Backed by existing standards (HL7, LOINC)
Positioned to handle FHIR-based resources Leverages current HIT technology

25 Pilot Teams DIGITizE is currently working with interested healthcare organizations who want to implement A pilot team typically involves organizational clinical and EMR/LIS IT staff and an outside reference lab

26 Where to Next Potential areas
FHIR-based decision support (aka CDS Hooks) Quality-based outcomes Content standardization

27 Questions?

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